It's a Long Story, but DDL Is Worth It. Data-Driven Learning as Multimodel Method for English Sessions in Turkish Prep-Classes

Autor/inn/en	Arslanbay, Goshnag; Ersanli, Ceylan Yangin
Titel	It's a Long Story, but DDL Is Worth It. Data-Driven Learning as Multimodel Method for English Sessions in Turkish Prep-Classes
Quelle	In: Journal on English Language Teaching, 13 (2023) 2, S.1-15 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2231-3338
Schlagwörter	English (Second Language); English for Academic Purposes; Second Language Learning; Second Language Instruction; Language of Instruction; Computational Linguistics; College Preparation; Foreign Countries; Data Analysis; State Universities; Case Studies; Digital Literacy; Learning Motivation; Native Speakers; Student Characteristics; Audio Equipment; Speech Communication; Learning Activities; Teaching Methods; Metalinguistics; Learner Engagement; Learning Problems; Technology Uses in Education; Instructional Effectiveness; Computer Software; Learning Management Systems; Learning Analytics; Turkey + Suchen Sie Ihr Suchwort? English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb; Fremdsprachenunterricht; Teaching language; Unterrichtssprache; Linguistics; Computerlinguistik; Ausland; Auswertung; Staatliche Universität; Case study; Fallstudie; Case Study; Motivation for studies; Lernmotivation; Muttersprachler; Audio-CD; Lernaktivität; Teaching method; Lehrmethode; Unterrichtsmethode; Metalanguage; Metasprache; Lernproblem; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Unterrichtserfolg; Türkei
Abstract	Data-Driven Learning (DDL) is a method for learning languages that involves analyzing language usage trends and finding patterns in language data, utilizing technology and statistics. One of the key benefits of DDL is that it allows students to focus on the most relevant and useful language data for their needs. Data-driven learning is an effective approach to language learning that can help students develop their language skills more quickly and efficiently by using data and technology to guide their learning. This case study aims to see if DDL has a positive effect on students' language achievement, digital literacy, and learning motivation. The study has enrolled 28 preparatory class students from a state university and seven native English speakers, comprising four Australians, one American, one Canadian, and one English individual. The native speakers are asked to verbally describe five images and respond to three pertinent questions. The audio recordings of their responses are transcribed by the students, and the data is then entered into AntConc, a corpus analysis toolkit. The students are able to investigate authentic English speech and recognize unknown linguistic structures. The study will clarify its findings and outcomes using quotations from the transcribed speech as well as the students' responses to the DDL activities. The findings imply that DDL is an effective method for teachers who are willing to experiment with alternative ways of teaching a language. It appears that using Data-Driven Learning (DDL) as a teaching strategy has produced largely positive results. Students seem to have responded favorably to the approach, and it has succeeded in increasing their awareness of language and how to study it. Additionally, it appears that DDL has improved learning circumstances over time for all students, particularly those who generally aren't motivated or engaged during traditional classes. The use of technology in the classroom has promoted group projects and boosted involvement from all students, including those with learning challenges. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01